R for Medicine and Biology

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Jones & Bartlett Learning, Oct 25, 2010 - Computers - 399 pages
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R is quickly becoming the number one choice for users in the fields of biology, medicine, and bioinformatics as their main means of storing, processing, sharing, and analyzing biomedical data. R for Medicine and Biology is a step-by-step guide through the use of the statistical environment R, as used in a biomedical domain. Ideal for healthcare professionals, scientists, informaticists, and statistical experts, this resource will provide even the novice programmer with the tools necessary to process and analyze their data using the R environment. Introductory chapters guide readers in how to obtain, install, and become familiar with R and provide a clear introduction to the programming language using numerous worked examples. Later chapters outline how R can be used, not just for biomedical data analysis, but also as an environment for the processing, storing, reporting, and sharing of data and results. The remainder of the book explores areas of R application to common domains of biomedical informatics, including imaging, statistical analysis, data mining/modeling, pathology informatics, epidemiology, clinical trials, and metadata usage. R for Medicine and Biology will provide you with a single desk reference for the R environment and its many capabilities.
 

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Contents

CHAPTER 1 R Installation and Getting Help
1
CHAPTER 2 The R Environment and Packages
5
CHAPTER 3 Basic Fundamentals of R
11
CHAPTER 4 Plotting Data
31
CHAPTER 5 Example Datasets
43
CHAPTER 6 Importing and Exporting Data in R
53
CHAPTER 7 R SQL and Database Connectivity
63
CHAPTER 8 Using R to Build a Biomedical Database in MySQL
75
CHAPTER 14 Survival Analysis
183
CHAPTER 15 Data Mining and Predictive Modeling with R and Weka
195
CHAPTER 16 Surveillance of Infectious Disease
233
CHAPTER 17 Medical Imaging and R
241
CHAPTER 18 Retrieving Public Microarray Datasets
271
CHAPTER 19 Working with Microarray Data
285
CHAPTER 20 Annotating Microarray Gene Lists
297
CHAPTER 21 Array CGH Analysis
309

CHAPTER 9 Creating Heterogeneous Datasets for Analysis in R
87
CHAPTER 10 Descriptive Statistics in R
105
CHAPTER 11 R and Basic Inferential Statistical Analysis
121
CHAPTER 12 Writing Functions in R
143
CHAPTER 13 Multivariate Analysis in R
149
CHAPTER 22 XML for Storing and Sharing Data
337
References
363
Appendix
367
Index
385
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